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Results tagged with machine-learning
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user 290553
Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.
1
vote
Wrong predictions with imbalanced binary data (regression logistic)
"And I found a few variables that are 99% correlated with the dependent variable"
Are you sure you're not using variable you shouldn't have in the time of your prediction ?
Example, if in your dataset …
- 176
2
votes
Why is ROC insensitive to class distributions?
Compared to the others, my answer is focused on understanding how you use ROC and AUC in Data Science cases. If you need the mathematical / statistical part, my answer won't help you.
Basically, ROC c …
- 176
1
vote
Should I run a machine learning model many times?
Computing it multiple times can be more useful for specific algorithms. Let's take an example : while a regression will do the same thing if you use it on the exact same data (ie if you split your tra …
- 176
1
vote
Encoding ID variables for machine learning
That's one of the important things in Data Science : you have to create your variables, good enough so your model can predict well.
Here, you pointed well, that you can't directly use IDs, but with pr …
- 176
0
votes
Improving Random Forest Classifier Design Python
Having too many variables can be a huge issue : if you have variables with less direct impact, they'll impact your Random Forest and make it less efficient. You have to find the right balance. Just so …
- 176
0
votes
Accepted
Smart Statistics vs Unsupervised Learning for Anomaly Detection
1-3 : Many things are possible, from unsupervised machine learning to stats : you can make really simple things (put thresholds by your own, calculate point distance from neighbours once you normalize …
- 176
0
votes
How does one ensure Machine Learning doesn't come to correct classifications via the wrong w...
This is the purpose of the Validation Set.
Split your dataset in 3 : Train, Test and Validation. Never touch your Validation set again until the last phase.
Create your model using Train and Test, tra …
- 176
0
votes
1
answer
44
views
Time Series : Caracterize how a time series differ from its past behaviour
I'm working with multiple time-series. Each time series is a record of a value (let's say a price) per month.
In each time-series, I have a reference period (let's say, 9 months, making it 9 records), …
- 176
0
votes
Training data size requirements for imbalanced class classification
AUC is not changing with class balancing, so unbalanced class won't lower your AUC value.
First, note than many models in sklearns have the option class_weight='balanced' that allows the model dealing …
- 176
0
votes
Should we use AUC as an indicator of overfitting when dataset is highly imbalanced?
One way to check if you're overfitting using AUC is also to apply it directly on your train data, and compare it to your validation. If you have a huge difference (with a train AUC way better, meaning …
- 176